






import itertools
import random
import unittest

import numpy as np

from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


__UpperCAmelCase      				=		random.Random()

if is_torch_available():
		import torch




def 	lowercase__ (	__snake_case       :				Any	,			__snake_case       :				Tuple=1.0	,			__snake_case       :				int=None	,			__snake_case       :				Optional[Any]=None    ):


					'''simple docstring'''


					if rng is None:
										UpperCAmelCase_       : Any				=		global_rng

					UpperCAmelCase_       : int				=		[]
					for batch_idx in range(shape[0]    ):
										values.append([]    )
										for _ in range(shape[1]    ):
															values[-1].append(rng.random() * scale    )

					return values



class     lowerCamelCase (unittest.TestCase     ):







		'''simple docstring'''



		def __init__(				self							,						_UpperCamelCase							,						_UpperCamelCase=7							,						_UpperCamelCase=4_0_0							,						_UpperCamelCase=2_0_0_0							,						_UpperCamelCase=1							,						_UpperCamelCase=0.0							,						_UpperCamelCase=1_6_0_0_0							,						_UpperCamelCase=True							,						_UpperCamelCase=True							,						)      ->				int:
							UpperCAmelCase_       : Optional[int]				=		parent
							UpperCAmelCase_       : Any				=		batch_size
							UpperCAmelCase_       : int				=		min_seq_length
							UpperCAmelCase_       : Optional[Any]				=		max_seq_length
							UpperCAmelCase_       : str				=		(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
							UpperCAmelCase_       : str				=		feature_size
							UpperCAmelCase_       : Any				=		padding_value
							UpperCAmelCase_       : Tuple				=		sampling_rate
							UpperCAmelCase_       : List[str]				=		return_attention_mask
							UpperCAmelCase_       : Tuple				=		do_normalize



		def    __UpperCAmelCase     (				self  )      ->				Any:
							return {
							    "feature_size": self.feature_size,
							    "padding_value": self.padding_value,
							    "sampling_rate": self.sampling_rate,
							    "return_attention_mask": self.return_attention_mask,
							    "do_normalize": self.do_normalize,
							}



		def    __UpperCAmelCase     (				self							,						_UpperCamelCase=False							,						_UpperCamelCase=False  )      ->				List[Any]:
							def _flatten(_UpperCamelCase  ):
												return list(itertools.chain(*_UpperCamelCase  )  )

							if equal_length:
												UpperCAmelCase_       : Tuple				=		floats_list((self.batch_size, self.max_seq_length)  )
							else:
												# make sure that inputs increase in size
												UpperCAmelCase_       : Tuple				=		[
												    _flatten(floats_list((x, self.feature_size)  )  )
												    for x in range(self.min_seq_length							,						self.max_seq_length							,						self.seq_length_diff  )
												]

							if numpify:
												UpperCAmelCase_       : Dict				=		[np.asarray(_UpperCamelCase  ) for x in speech_inputs]

							return speech_inputs





@require_torch
@require_torchaudio
class     lowerCamelCase (__lowercase					,    unittest.TestCase     ):







		'''simple docstring'''



		_snake_case       :       Optional[int]					=    ASTFeatureExtractor



		def    __UpperCAmelCase     (				self  )      ->				Any:
							UpperCAmelCase_       : List[str]				=		ASTFeatureExtractionTester(self  )



		def    __UpperCAmelCase     (				self  )      ->				Union[str, Any]:
							# Tests that all call wrap to encode_plus and batch_encode_plus
							UpperCAmelCase_       : Optional[Any]				=		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
							# create three inputs of length 800, 1000, and 1200
							UpperCAmelCase_       : Optional[int]				=		[floats_list((1, x)  )[0] for x in range(8_0_0							,						1_4_0_0							,						2_0_0  )]
							UpperCAmelCase_       : Optional[Any]				=		[np.asarray(_UpperCamelCase  ) for speech_input in speech_inputs]

							# Test not batched input
							UpperCAmelCase_       : Union[str, Any]				=		feat_extract(speech_inputs[0]							,						return_tensors='np'  ).input_values
							UpperCAmelCase_       : Dict				=		feat_extract(np_speech_inputs[0]							,						return_tensors='np'  ).input_values
							self.assertTrue(np.allclose(_UpperCamelCase							,						_UpperCamelCase							,						atol=1E-3  )  )

							# Test batched
							UpperCAmelCase_       : List[str]				=		feat_extract(_UpperCamelCase							,						padding=_UpperCamelCase							,						return_tensors='np'  ).input_values
							UpperCAmelCase_       : str				=		feat_extract(_UpperCamelCase							,						padding=_UpperCamelCase							,						return_tensors='np'  ).input_values
							for enc_seq_a, enc_seq_a in zip(_UpperCamelCase							,						_UpperCamelCase  ):
												self.assertTrue(np.allclose(_UpperCamelCase							,						_UpperCamelCase							,						atol=1E-3  )  )

							# Test 2-D numpy arrays are batched.
							UpperCAmelCase_       : Optional[int]				=		[floats_list((1, x)  )[0] for x in (8_0_0, 8_0_0, 8_0_0)]
							UpperCAmelCase_       : Optional[Any]				=		np.asarray(_UpperCamelCase  )
							UpperCAmelCase_       : Tuple				=		feat_extract(_UpperCamelCase							,						return_tensors='np'  ).input_values
							UpperCAmelCase_       : List[str]				=		feat_extract(_UpperCamelCase							,						return_tensors='np'  ).input_values
							for enc_seq_a, enc_seq_a in zip(_UpperCamelCase							,						_UpperCamelCase  ):
												self.assertTrue(np.allclose(_UpperCamelCase							,						_UpperCamelCase							,						atol=1E-3  )  )



		@require_torch
		def    __UpperCAmelCase     (				self  )      ->				Union[str, Any]:
							import torch

							UpperCAmelCase_       : List[str]				=		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
							UpperCAmelCase_       : Optional[Any]				=		np.random.rand(1_0_0  ).astype(np.floataa  )
							UpperCAmelCase_       : str				=		np_speech_inputs.tolist()

							for inputs in [py_speech_inputs, np_speech_inputs]:
												UpperCAmelCase_       : Optional[Any]				=		feature_extractor.pad([{'input_values': inputs}]							,						return_tensors='np'  )
												self.assertTrue(np_processed.input_values.dtype == np.floataa  )
												UpperCAmelCase_       : Dict				=		feature_extractor.pad([{'input_values': inputs}]							,						return_tensors='pt'  )
												self.assertTrue(pt_processed.input_values.dtype == torch.floataa  )



		def    __UpperCAmelCase     (				self							,						_UpperCamelCase  )      ->				Dict:
							from datasets import load_dataset

							UpperCAmelCase_       : Any				=		load_dataset('hf-internal-testing/librispeech_asr_dummy'							,						'clean'							,						split='validation'  )
							# automatic decoding with librispeech
							UpperCAmelCase_       : Tuple				=		ds.sort('id'  ).select(range(_UpperCamelCase  )  )[:num_samples]["audio"]

							return [x["array"] for x in speech_samples]



		@require_torch
		def    __UpperCAmelCase     (				self  )      ->				Tuple:
							# fmt: off
							UpperCAmelCase_       : Optional[int]				=		torch.tensor(
							    [-0.98_94, -1.27_76, -0.90_66, -1.27_76, -0.93_49, -1.26_09, -1.03_86, -1.27_76,
							     -1.15_61, -1.27_76, -1.20_52, -1.27_23, -1.21_90, -1.21_32, -1.27_76, -1.11_33,
							     -1.19_53, -1.13_43, -1.15_84, -1.22_03, -1.17_70, -1.24_74, -1.23_81, -1.19_36,
							     -0.92_70, -0.83_17, -0.80_49, -0.77_06, -0.75_65, -0.78_69]  )
							# fmt: on

							UpperCAmelCase_       : List[Any]				=		self._load_datasamples(1  )
							UpperCAmelCase_       : Any				=		ASTFeatureExtractor()
							UpperCAmelCase_       : Any				=		feature_extractor(_UpperCamelCase							,						return_tensors='pt'  ).input_values
							self.assertEquals(input_values.shape							,						(1, 1_0_2_4, 1_2_8)  )
							self.assertTrue(torch.allclose(input_values[0, 0, :3_0]							,						_UpperCamelCase							,						atol=1E-4  )  )








import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     Optional[int]		,    _lowerCamelCase					:     List[Any]		,    _lowerCamelCase					:     Optional[Any])   ->       Dict:






							'''simple docstring'''





							__UpperCamelCase      :  str       =   BertConfig.from_json_file(_lowerCamelCase)
							print(F'Building PyTorch model from configuration: {config}')
							__UpperCamelCase      :  Optional[Any]       =   BertForPreTraining(_lowerCamelCase)

							# Load weights from tf checkpoint
							load_tf_weights_in_bert(_lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase)

							# Save pytorch-model
							print(F'Save PyTorch model to {pytorch_dump_path}')
							torch.save(model.state_dict()		,    _lowerCamelCase)


if __name__ == "__main__":
		lowercase    :   Union[str, Any]						=			argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
		)
		parser.add_argument(
		    '--bert_config_file',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        'The config json file corresponding to the pre-trained BERT model. \n'
		        'This specifies the model architecture.'
		    ),
		)
		parser.add_argument(
		    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
		)
		lowercase    :   Any						=			parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)